US5887035A  Method for joint equalization and detection of multiple user signals  Google Patents
Method for joint equalization and detection of multiple user signals Download PDFInfo
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 US5887035A US5887035A US08962249 US96224997A US5887035A US 5887035 A US5887035 A US 5887035A US 08962249 US08962249 US 08962249 US 96224997 A US96224997 A US 96224997A US 5887035 A US5887035 A US 5887035A
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 H—ELECTRICITY
 H04—ELECTRIC COMMUNICATION TECHNIQUE
 H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
 H04L25/00—Baseband systems
 H04L25/02—Details ; Arrangements for supplying electrical power along data transmission lines
 H04L25/03—Shaping networks in transmitter or receiver, e.g. adaptive shaping networks ; Receiver end arrangements for processing baseband signals
 H04L25/03006—Arrangements for removing intersymbol interference
 H04L25/03178—Arrangements involving sequence estimation techniques
 H04L25/03331—Arrangements for the joint estimation of multiple sequences

 H—ELECTRICITY
 H04—ELECTRIC COMMUNICATION TECHNIQUE
 H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
 H04L25/00—Baseband systems
 H04L25/02—Details ; Arrangements for supplying electrical power along data transmission lines
 H04L25/03—Shaping networks in transmitter or receiver, e.g. adaptive shaping networks ; Receiver end arrangements for processing baseband signals
 H04L25/03006—Arrangements for removing intersymbol interference
 H04L25/03178—Arrangements involving sequence estimation techniques
 H04L25/03248—Arrangements for operating in conjunction with other apparatus
 H04L25/03292—Arrangements for operating in conjunction with other apparatus with channel estimation circuitry
Abstract
Description
The present invention relates generally to signal processing in a telecommunications system, and, more particularly, to methods for joint equalization and detection of symbol sequences.
The basic function of a communication system is to send information from a source that generates the information to one or more destinations. In a radio communication system, a number of obstacles must be overcome to successfully transmit and receive information including channel distortion which may cause, for example, intersymbol interference (ISI) and additive noise. The receiver must compensate for the effects of channel distortion and the resulting intersymbol interference. This is usually accomplished by means of an equalizer/detector at the receiver.
A number of different equalization schemes have been used in the past to eliminate or minimize intersymbol interference. The most commonly used approaches include decision feedback equalization (DFE) and maximum likelihood sequence estimation (MLSE). In a maximum likelihood sequence estimation equalization scheme, a detector produces the most probable symbol sequence for the given received sample sequence . An algorithm for implementing maximum likelihood sequence detection is the Viterbi algorithm, which was originally devised for decoding convolutional codes. The application of the Viterbi algorithm to the problem of sequence detection in the presence of ISI is described by Gottfried Ungerboeck in "Adaptive Maximum Likelihood Receiver for Carrier Modulated Data Transmission Systems," IEEE Transactions on Communications, Volume COM22, #5, May 1974. This MLSE approach employs a matched filter followed by a MLSE algorithm and an auxiliary channel estimation scheme.
A major drawback of using maximum likelihood sequence detection for communications channels which may experience intersymbol interference is that the computational complexity grows exponentially as a function of the span of the intersymbol interference and the number of interfering users. Consequently, maximum likelihood sequence detection is practical only for single user signals where the intersymbol interference spans only a few symbols. Therefore, there is great interest in finding new approaches to sequence estimation which reduce the computational complexity associated with maximum likelihood sequence detection.
The present invention provides an iterative method for generating estimates of a transmitted symbol sequence. The transmitted symbol sequence is received over a communication channel which may result in distortion of the waveform. Each possible transmitted symbol is represented by an indicator vector which is used to select a possible transmitted symbol from a symbol library vector. An estimate of the indicator vector, called the estimated symbol probability vector, is calculated and used to select an estimate of the transmitted symbol.
The t'th element of the estimated symbol probability vector is an estimate of the probability that the t'th symbol in the symbol library vector was transmitted. To form the estimated symbol probability vector, channel estimates are generated representing the estimated impulse response of the channel for each transmitted symbol. The channel estimates are used to form a matched medium response vector and an interaction matrix.
The matched medium response vector is formed by a combination of the received signal, the channel estimates, and the symbol library vector and represents the matched medium response component for the possible transmitted symbols in the symbol library vector. The interaction matrix is based upon the channel estimates and the symbol library vector and contains the interaction between interfering symbols. The matched medium response vector and the interaction matrix are used to derive the estimated symbol probability vector for each symbol in the transmitted symbol sequence. The estimated symbol probability vector is used to select an estimate of each transmitted symbol in said symbol sequence from the symbol library vector.
The estimated symbol probability vector is formed by recursively estimating each element of each estimated symbol probability vector based upon the matched medium response vector, the interaction matrix and a previous estimate of the estimated symbol probability vector. The recursive estimation of each element of each estimated symbol probability vector is performed until some predefined convergence criteria is satisfied or for a predetermined number of iterations. The recursive nature of the estimation process results in maximum a posteriori estimates of the indicator vector which may be used to select an estimate of the transmitted symbol from the symbol library vector.
The primary advantage of the present invention is a reduction in overall complexity. Using the iterative approach of the present invention, the state space is of the order (N_{u} ×N_{b}) as opposed to N_{b} ^{Nu} in the conventional Viterbi algorithm. This reduction in overall complexity is at the expense of using multiple iterations in the algorithm. The reduction in computational complexity allows for joint demodulation of multiple user signals simultaneously.
FIG. 1 is a block diagram of a digital communication system.
FIG. 2 is a block diagram of a detector in accordance with the present invention.
FIG. 3 is a block diagram of an estimated indicator vector generator.
FIG. 4 is a flow diagram illustrating the detection method of the present invention.
FIG. 1 illustrates a radio transmission system for digital signal transmission. The system consists of a transmitter 12 having an impulse response F(t), a communication channel 14 with additive white Gaussian noise (AWGN), a receiver 16 with impulse response f(t), a sampler 18 that periodically samples the output of the receiver 16, and a symbol detector 20. In general, the transmitter 12 receives an input data sequence which represents information to be transmitted. The input data sequence is assumed to be in digital form. The transmitter 16 modulates a carrier signal with the input sequence using a Mary modulation scheme such as pulse amplitude modulation (PAM) or quadrature amplitude modulation (QAM). The input data sequence is subdivided into k bit symbols, and each symbol is represented by a distinct signal waveform referred to herein as the transmitted signal. The transmitted signal is subjected to disturbances in the communication channel 14 which may result in phase and/or amplitude distortion. The transmitted signal is also degraded by added noise. The channel corrupted signal is received at the receiver 16. The signal received at the receiver is referred to herein as the received signal y_{i}.
The receiver 16 has an impulse response f(t) which is ideally matched to the impulse response F(t) of the transmitter 12. In actual practice, the ideal receiver 16 may not be realizable, so an approximation of the ideal receiver 16 may be used. The output of the receiver 16 is sampled periodically by the sampling circuit 18. A timing signal extracted from the received signal y_{i} is used as a clock for sampling the received signal y_{i}. The sampled and filtered received signal y_{i} is passed to the detector 20. The purpose of the detector 20 is to estimate a transmitted sequence s_{m} which is contained in the received signal y_{i}.
In a digital communication system that transmits information over a channel that causes ISI, the optimum detector is a maximum likelihood symbol sequence detector (MLSD) which produces the most probable symbol sequence for a given received symbol sequence. A decision rule is employed based on the computation of the posterior probabilities defined as
P(s.sub.m y.sub.i), m=1,2, . . . , M (1)
where s_{m} is the transmitted sequence, and y0i is the received signal. The decision criterion is based on selecting the sequence corresponding to the maximum of the set of posterior probabilities {P(s_{m})}. This criterion maximizes the probability of a correct decision and, hence, minimizes the probability of error. This decision criterion is called the maximum a posteriori probability (MAP) criterion.
Using Bayes rule, the posterior probabilities may be expressed as ##EQU1## where f(y_{i} s_{m}) is a conditional probability density function (p.d.f.) of the observed vector y given the transmitted sequence s_{m}, and P(s_{m}) is the a priori probability of the m^{th} signal being transmitted. Computation of the posterior probabilities P(s_{m} y_{i}) requires knowledge of the a priori probabilities P(s_{m}) and the conditional p.d.f. f(y_{i} s_{m}) for m=1,2, . . . ,M. The conditional p.d.f. is called the likelihood function.
Some simplification occurs in the MAP criterion when the M signals are equally probable a priori, i.e., P(s_{m})=1/M for all M. Furthermore, it should be noted that the denominator in Equation (2) is independent of which signal of the M signals is transmitted. Consequently, the decision rule based on finding the signal that maximizes the posterior probability P(s_{m} y_{i}) is equivalent to finding the signal that maximizes the likelihood function f(y_{i} s_{m}).
In the case of a faded and dispersive channel with additive, white Guassian noise AWGN, the received signal is assumed to be in the form: ##EQU2## where y_{i} is the received signal at time i, s_{l} is the l'th transmitted symbol, g_{il},l is a channel impulse response that symbol s. contributes to the received signal at time i, and w_{i} is the noise at time i. This model corresponds to a received signal y_{i} having symbolspaced echoes and Nyquist matched filtering.
A conventional loglikelihood function used in the receiver is written as follows: ##EQU3##
The term r_{l} represents a matched medium response of the receiver to both the transmitter and the channel for symbol l. The term h_{l},m represents the complex ISI between adjacent symbols generated by the multipath component and the pulseshape waveform. The symbol g,_{il},l^{H} is the conjugate transpose of the channel impulse response .
The method of the present invention represents the l'th symbol, s_{l}, received during a given sampling interval, as a function of two vectors which can be described mathematically as follows:
s.sub.l =Z.sub.l.sup.T B.sub.l (7)
where Z_{l} is an indicator vector and B_{l} is a symbol library vector. It should be noted that the symbol library vector B_{l} may be fixed for all values of l, in which case the modulation does not vary over time (e.g. QPSK). The symbol library vector B_{l} could be varied over time to represent a coded modulation sequence. The symbol library vector B_{l} contains as elements all possible transmitted symbols. The indicator vector Z_{l} contains a plurality of elements consisting of a single element equal to "1" and all remaining elements equal to "0". The number of elements in the indicator vector Z_{l} is equal to the number of elements (or symbols) in the symbol library vector B_{l}. Consequently, the position of the "1" element within the indicator vector uniquely identifies a symbol which is located at the same position in the symbol library vector B_{l} For example, an indicator vector with a "1" element in the 3'rd position indicates that the 3'rd element of the symbol library vector B_{l} was transmitted.
Using indicator vector notation and substituting the variables U and V, the loglikelihood function of equation (4) becomes: ##EQU4## is a matched medium response vector for the possible transmitted symbols b_{l} ; and
V.sub.l,m =B.sub.l.sup.* B.sub.l.sup.T h.sub.l,m (10)
is an an interaction matrix that represents the ISI between the received symbols and their neighbor symbols. The term neighbor symbols is used herein to mean symbols in the local neighborhood of a given received symbol which induce ISI in the given received symbol. As will be described below, the loglikelihood function of equation (8) is used as a convergence criteria in the iterative calculation of the estimated symbol probability vector Z_{l} for a given received symbol s_{l}.
In accordance with the present invention, an iterative approach is used to find an estimate Z_{l} of the indicator vector Z_{l} for each transmitted symbol. This estimate is referred to herein as the estimated symbol probability vector Z_{l} and has t'th element Z_{l},l.sup.(p) given by the equation: ##EQU5## where e_{l} represents an indicator vector having a "1" as the value of the t'th element, while all other elements have a value of "0". The term Z_{m}.sup.(p1) represents estimated symbol probability vectors for the previous iteration of the neighbor (i.e. interfering) symbols. N_{b} is the number of possible symbols in the symbol library vector B_{l} and n_{l} defines the estimated indicator vectors Z_{m} in the local neighborhood of s_{l}. The numerator of equation (11) is referred to as the partial metric parameter and the denominator is referred to as the normalizing parameter.
The approach to calculating the estimated symbol probability vector Z_{l}.sup.(p) and hence the received symbol s_{l}, is recursive or iterative in nature to account for the interference from adjacent sampling intervals, also known as ISI. ISI, by definition, implies that a given sampling interval not only contains information related to the symbol directly associated with that sampling interval, but also contains information related to symbols received in adjacent sampling intervals. Consequently, in order to accurately estimate the value of a symbol at a given sampling interval, knowledge of the contents, or estimated contents, of adjacent sampling intervals is also required. Equation (11) defines a recursive relation between the estimate of the symbol probability vector for a particular symbol at the p'th iteration and the estimated symbol probability vectors corresponding to the interfering neighbors of that symbol at the previous (p1)'th iteration.
In the initial iteration (p=1) of equation (11), an assumed value for each element of each estimated symbol probability vector Z_{m}.sup.(o) is used. After the first iteration (p>1), the estimated symbol probability vectors Z_{l} from the (p1)'st iteration are used in the calculation and are designated in equation (11) as Z_{m}. Each iteration results in an updated set of estimated symbol probability vectors Z_{l}, one for each symbol being estimated. This process is repeated for a predetermined number of iterations or until some predefined convergence criteria is satisfied.
In the iterative calculation of estimated symbol probability vectors Z_{l}, some function f(Z_{m}.sup.(p1))of the previous iteration of the estimated symbol probablity vectors Z_{m}.sup.(p1) in equation (11) could be used in place of the estimated symbol probablity vectors Z_{m}.sup.(p1). This function f(Z_{m}.sup.(p1) may output a hard decision in which each element of the estimated symbol probablity vector Z_{m}.sup.(p1) takes on a value of 0 or 1. Alternatively, the function f(Z_{m}.sup.(p1)) could be a  continuous nonlinear function that produces an output for each element of the estimated symbol probablity vector Z_{m}.sup.(p1) in the range from 0 to 1, where the sum across all elements is exactly 1.
As described above, during each iteration a complete set of estimated symbol probability vectors Z_{l} must be calculated. Consequently, application of equation (11) is required N_{b} times for each of the L members of the estimated symbol probability vector Z_{l}. That is, since there are L sampling intervals under consideration, there are consequently L distinct estimated symbol probability vectors, Z_{l} through Z_{l}, with each vector containing as many elements as there are symbols in the symbol library, N_{b}. At the conclusion of each iteration, there will be a set of estimated symbol probability vectors corresponding to each of the transmitted symbols. A determination is then made whether an additional iteration is required. If so, the calculations of the estimated symbol probability vectors is repeated resulting in updated estimates. This process is repeated for a predetermined number of iterations or until some predefined convergence criteria is satisfied.
The loglikelihood equation (8) may, for example, be used to test for convergence. After each iteration, the entire set of estimated symbol probability vectors {Z_{1},Z_{2}, . . . Z_{L} } is applied to the loglikelihood equation (8) resulting in a numerical value which reflects the likelihood that the set of estimated symbol probability vectors {Z_{1},Z_{2}, . . . Z_{L} }, and more specifically the symbols that they represent, accurately reflect the actual symbols sent by the transmitter. When further iteration is deemed unnecessary, the set of estimated symbol probability vectors {Z_{1},Z_{2}, . . . Z_{L} } can be queried as to their contents. The individual elements of the estimated symbol probability vectors Z_{l} may take on real values ranging from 0 to 1, and reflect the probabilities that the corresponding symbols in the associated symbol library vector B_{l} were transmitted. These probabilities may then be used to assist in performing either a softdecision or a hard decision with regard to the symbol or symbol sequence in question.
To those skilled in the art it will become apparent that the detection method of the present invention, which is described above for the case of a single user signal, can easily be extended to include the case of multiple user signal. An implementation of the multiple user configuration contemplated would necessarily require minor alterations to the mathematical expressions previously described for the single user configuration. However, the basic functional intent of these expressions remains unchanged.
Specifically, in the multiple user instance, equation (3) would appear as follows: ##EQU6## where an additional summation over the entire user population, N_{u}, has been added. Likewise, equation (4) assumes the following form in the multiple user scenario: ##EQU7## In this case, s_{k},u is the l'th symbol for user u, and g_{kl},l (u) is a channel impulse response contributing to the received signal at time l and delay kl for the user u. The term h_{l}.m (u,r) represents the interaction between users u and v. Once again, only additional summations over the user population, N_{u}, have been added.
When converted to indicator vector form, the loglikelihood equations becomes: ##EQU8## The estimated symbol probability equation becomes: ##EQU9##
FIG. 2 illustrates the detector 20 for detecting a received signal y_{i} from multiple users. The detector 20 includes a channel estimator 22, a matched medium response vector generator 24, and a symbol interaction matrix generator 30. The channel estimator 22 generates an estimate of the channel impulse response g_{il},l based on the received signal y_{i}. The term g_{il},l represents the channel impulse response corresponding to the l'th symbol with a delay factor of il. The estimate of the channel impulse response g_{il},l is computed in a known manner. Known training symbols can be used to generate channel estimates. Alternatively, hard or soft detected symbols can also be used to generate channel estimates. The estimate of the channel impulse response g_{il},l is passed to the matched medium response vector generator 24 and the _symbol interaction matrix generator 30.
The matched medium response vector generator 24 includes a matched medium response filter 26 and a vector generator 28. The matched medium response filter 26 computes the matched medium response r_{l},u to both the transmitter and the channel medium for symbol l for each user u given the received signal y0i and the estimate of the channel impulse response g_{il},l (u). The matched medium response r_{l},u is then passed to the vector generator 28, which in conjunction with knowledge of a symbol library 36 computes a matched medium response vector, U_{l},u.
The symbol interaction matrix generator 30 includes an interaction coefficient generator 32 and a matrix generator 34. The interaction coefficient generator 32 computes the symbol interaction coefficient, h_{l},m (U,v), which is indicative of the channel response for symbols l and m for each user pair u,v. The symbol interaction coefficient h_{l},m (u,v) is passed to the matrix generator 34 which, in coordination with information in the symbol library 36, generates the symbol interaction matrix, V_{l},m. The matched medium response vector, U_{i},u, and the symbol interaction matrix, V_{l},m (u,v), are finally passed to an estimated symbol probability vector generator 40, which computes the estimated symbol probability vector, Z_{l} in accordance with equation (16).
Shown in FIG. 3 is a more detailed schematic diagram of the estimated symbol probability vector generator 40. The estimated symbol probability vector generator 40 is comprised of an iterative processor 42, an iteration and output controller 46, and a previous iteration buffer 44. The iterative processor 42 is responsible for implementing and solving the mathematical expressions that describe and define the estimated symbol probability vector, Z_{l},u, for a given symbol, l and user u in the multiuser embodiment. The output of the iterative processor 42 is an estimated indicator vector, Z_{l},u, which is received and analyzed by the iteration and output controller 46.
The controller 46 implements a particular strategy for determining whether an additional iteration is required. Potential iteration control strategies could include defining a fixed number of iterations, or the observing and monitoring a Z dependent function, such as the loglikelihood function previously described. In such a case, the decision regarding the need for an additional iteration could be determined by observing the mean squared error between successively computed Z_{l} vectors. Should the controller 46 determine that an additional iteration is required, the newly computed set of estimated symbol probability vectors, {Z_{1},Z_{2}, . . . Z_{l} }, is temporarily stored in the iteration buffer 44. The set of estimated symbol probability vectors {Z_{1},Z_{2}, . . . Z_{l} } stored in the iteration buffer 44 are then used in the computation of estimated symbol probability vectors during the next iteration.
Regardless of the iteration control strategy implemented, the function of the controller 46 is to administer the implemented iteration termination criteria, and ultimately output the iteratively optimized estimated symbol probability vector, Z_{l},u The estimated symbol probability vector Z_{l},u may be used by a harddecision generator 48 to make a harddecision about estimated symbol probability vector Z_{l},u into estimated symbol s_{l},u The estimated symbol s_{l},u is then output from the harddecision generator 48 to a decoder. Alternatively, the estimated symbol probability vector Z_{l},u may be passed directly to a softdecision decoder.
FIG. 4 is a flow diagram illustrating the operation of the detector 20 of the present invention. After receiving the received signal y_{i}, the detector 20 computes channel estimates g_{il},l which represent the estimated impulse response of the communication channel to the transmitted symbols (block 100). The detector 20 then computes the matched medium response vector U_{l},u (block 102) and the symbol interaction coefficients matrix V_{l},u (block 104). The matched medium response vector U_{l},u is calculated based on the channel estimates g_{il},l and the received signal y_{i}. The symbol interaction coefficients matrix V_{l},u is computed based on the channel estimates g_{il},l and represents the interaction between adjacent symbols. The matched medium response vectors U_{l},u and the symbol interaction coefficient matrix V_{l},u are then used to compute estimated symbol probability vectors {Z_{0},Z_{1},Z_{2} . . . } for each received symbol (block 106). The loglikelihood for the set of estimated symbol probability vectors is then calculated using the loglikelihood equation (block 108). The loglikelihood results in a numerical value which reflects the likelihood that the set of estimated symbol probability vectors {Z_{0},Z_{1},Z_{2} . . . } accurately represents the set of transmitted symbols. The detector 20 then determines whether another iteration is required (block 110), using, for example, some predetermined convergence criteria. If so, a new set of estimated symbol probability vectors for each received symbol are computed (block 106). The log likelihood based on this new set of estimated symbol probability vectors is then calculated (block 108). This process is repeated until the detector 20 determines that no further iterations are required (block 110). The final set of estimated symbol probability estimates are then used to generate hard decision estimates of the transmitted symbols, or, alternatively, are output to a soft decision decoder (block 112).
Based on the foregoing, it is seen that the present invention provides an alternative approach to finding maximum likelihood sequence estimates which may be considered when the state space is too large for a conventional Viterbi detector. The present invention may, of course, be carried out in other specific ways than those herein set forth without departing from the spirit and essential characteristics of the invention. The present embodiments are, therefore, to be considered in all respects as illustrative and not restrictive, and all changes coming within the meaning and equivalency range of the appended claims are intended to be embraced therein.
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AU1115099A AU747161B2 (en)  19971031  19981022  Method for joint equalization and detection of multiple user signals 
DE1998639967 DE69839967D1 (en)  19971031  19981022  Process for the simultaneous equalization and detection of multiple user signals 
KR20007004721A KR20010031665A (en)  19971031  19981022  Method for joint equalization and detection of multiple user signals 
EP19980953895 EP1029409B1 (en)  19971031  19981022  Method for joint equalization and detection of multiple user signals 
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CN1278382A (en)  20001227  application 
WO1999023795A1 (en)  19990514  application 
CA2308173A1 (en)  19990514  application 
KR20010031665A (en)  20010416  application 
EP1029409A1 (en)  20000823  application 
JP4329974B2 (en)  20090909  grant 
DE69839967D1 (en)  20081016  grant 
EP1029409B1 (en)  20080903  grant 
JP2001522197A (en)  20011113  application 
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